Re: Looking for packages to do Feature Selection and Classifi cation

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Re: Looking for packages to do Feature Selection and Classifi cation

Tuszynski, Jaroslaw W.
Hi,

You should also check my msc.features.select from caMassClass package. It
has feature selection algorithm that I found useful in case of mass-spectra
data. It performs individual feature selection and/or removes highly
correlated neighbor features.

Jarek

-----Original Message-----
From: [hidden email]
[mailto:[hidden email]]
Sent: Friday, January 06, 2006 3:12 PM
To: Weiwei Shi
Cc: Diaz.Ramon; r-help
Subject: Re: [R] Looking for packages to do Feature Selection and
Classification

Thanks. It's indeed an interesting paper. Besides RF (using Ramon's varSelRF
package), I am also testing Guyon et al's (2002) Recursive Feature
Elimination for my feature-selection part.

On 1/5/06, Weiwei Shi <[hidden email]> wrote:

>
> FYI:
>
> check the following paper on svm (using libsvm) as well as random
> forest in the context of feature selection.
>
> http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf
>
> HTH
>
> On 1/4/06, Diaz.Ramon <[hidden email]> wrote:
> > Dear Frank,
> > I expect you'll get many different answers since a wide variety of
> approaches have been suggested. So I'll stick to self-advertisment: I've
> written an R package, varSelRF (available from R), that uses random forest
> together with a simple variable selection approach, and provides also
> bootstrap estimates of the error rate of the procedure. Andy Liaw and
> collaborators previously developed and published a somewhat similar
> procedure. You probably also want to take a look at several packages
> available from BioConductor.
> >
> > Best,
> >
> > R.
> >
> >
> > -----Original Message-----
> > From:   [hidden email] on behalf of Frank Duan
> > Sent:   Wed 1/4/2006 4:23 AM
> > To:     r-help
> > Cc:
> > Subject:        [R] Looking for packages to do Feature Selection and
> Classification
> >
> > Hi All,
> >
> > Sorry if this is a repost (a quick browse didn't give me the answer).
> >
> > I wonder if there are packages that can do the feature selection and
> > classification at the same time. For instance, I am using SVM to
> classify my
> > samples, but it's easy to get overfitted if using all of the features.
> Thus,
> > it is necessary to select "good" features to build an optimum hyperplane
> > (?). Here is a simple example: Suppose I have 100 "useful" features and
> 100
> > "useless" features (or noise features), I want the SVM to give me the
> > same results when 1) using only 100 useful features or 2) using all 200
> > features.
> >
> > Any suggestions or point me to a reference?
> >
> > Thanks in advance!
> >
> > Frank
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
> >
> > --
> > Ramón Díaz-Uriarte
> > Bioinformatics Unit
> > Centro Nacional de Investigaciones Oncológicas (CNIO)
> > (Spanish National Cancer Center)
> > Melchor Fernández Almagro, 3
> > 28029 Madrid (Spain)
> > Fax: +-34-91-224-6972
> > Phone: +-34-91-224-6900
> >
> > http://ligarto.org/rdiaz
> > PGP KeyID: 0xE89B3462
> > (http://ligarto.org/rdiaz/0xE89B3462.asc)
> >
> >
> >
> > **NOTA DE CONFIDENCIALIDAD** Este correo electrónico, y en
> s...{{dropped}}
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
> >
>
>
> --
> Weiwei Shi, Ph.D
>
> "Did you always know?"
> "No, I did not. But I believed..."
> ---Matrix III
>

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